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Roundtable on Engineering Excellence -

Date

November 2024

Engineering excellence remained a critical focus for Financial Services Institutions aiming to stay competitive and innovative in an ever-evolving landscape. With the rise of GenAI, there was growing interest in understanding how it could be leveraged to enhance the software development lifecycle and improve engineering productivity.

The event highlighted key insights, including lessons learned from a leading technology organization that had transformed its software engineering practices using GenAI. It also showcased data-backed research from a Stanford lab on measuring engineering productivity and facilitated peer-to-peer discussions on common challenges and best practices. Attendees left with a deeper understanding of how to use GenAI to drive enterprise value and advance engineering excellence.

https://mckinsey.cventevents.com/event/a3d50d53-9a86-4d60-93b0-ee27da7072b5/summary?tm=ljnVDSL9f4kK0SZk_eynztctbKrxr5LpsFkrxP3Mlz0

More Research & Conferences

DPE Summit 2024: Measuring Software Engineering Productivity

Approaches to Measuring Software Engineering Productivity

DPE Summit 2024: Measuring Software Engineering Productivity

Approaches to Measuring Software Engineering Productivity

DPE Summit 2024: Measuring Software Engineering Productivity

Approaches to Measuring Software Engineering Productivity

Research on predicting expert valuations in code review

Manual code reviews are an essential but time-consuming part of software development, often leading reviewers to prioritize technical issues while skipping valuable assessments. This paper presents an algorithmic model that automates aspects of code review typically avoided due to their complexity or subjectivity, such as assessing coding time, implementation time, and code complexity. Instead of replacing manual reviews, our model adds insights that help reviewers focus on more impactful tasks. Calibrated using expert evaluations, the model predicts key metrics from code commits with strong correlations to human judgments (r = 0.82 for coding time, r = 0.86 for implementation time). By automating these assessments, we reduce the burden on human reviewers and ensure consistent analysis of time-consuming areas, offering a scalable solution alongside manual reviews. This research shows how automated tools can enhance code reviews by addressing overlooked tasks, supporting data-driven decisions and improving the review process.

Research on predicting expert valuations in code review

Manual code reviews are an essential but time-consuming part of software development, often leading reviewers to prioritize technical issues while skipping valuable assessments. This paper presents an algorithmic model that automates aspects of code review typically avoided due to their complexity or subjectivity, such as assessing coding time, implementation time, and code complexity. Instead of replacing manual reviews, our model adds insights that help reviewers focus on more impactful tasks. Calibrated using expert evaluations, the model predicts key metrics from code commits with strong correlations to human judgments (r = 0.82 for coding time, r = 0.86 for implementation time). By automating these assessments, we reduce the burden on human reviewers and ensure consistent analysis of time-consuming areas, offering a scalable solution alongside manual reviews. This research shows how automated tools can enhance code reviews by addressing overlooked tasks, supporting data-driven decisions and improving the review process.

Research on predicting expert valuations in code review

Manual code reviews are an essential but time-consuming part of software development, often leading reviewers to prioritize technical issues while skipping valuable assessments. This paper presents an algorithmic model that automates aspects of code review typically avoided due to their complexity or subjectivity, such as assessing coding time, implementation time, and code complexity. Instead of replacing manual reviews, our model adds insights that help reviewers focus on more impactful tasks. Calibrated using expert evaluations, the model predicts key metrics from code commits with strong correlations to human judgments (r = 0.82 for coding time, r = 0.86 for implementation time). By automating these assessments, we reduce the burden on human reviewers and ensure consistent analysis of time-consuming areas, offering a scalable solution alongside manual reviews. This research shows how automated tools can enhance code reviews by addressing overlooked tasks, supporting data-driven decisions and improving the review process.

McKinsey Roundtable: Software Engineering Excellence

This exclusive gathering brought together 20-30 engineering leaders from tech, retail, consumer, industrials, and MedTech sectors to explore cutting-edge approaches to productivity.

McKinsey Roundtable: Software Engineering Excellence

This exclusive gathering brought together 20-30 engineering leaders from tech, retail, consumer, industrials, and MedTech sectors to explore cutting-edge approaches to productivity.

McKinsey Roundtable: Software Engineering Excellence

This exclusive gathering brought together 20-30 engineering leaders from tech, retail, consumer, industrials, and MedTech sectors to explore cutting-edge approaches to productivity.